An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations
Not on the map. Why?
Abstract (arXiv)
In this paper, we introduce a new framework for approximately solving flow problems in capacitated, undirected graphs and apply it to provide asymptotically faster algorithms for the maximum s-t flow and maximum concurrent multicommodity flow problems. For graphs with n vertices and m edges, it allows us to find an ε-approximate maximum s-t flow in time O(m1+o(1)ε-2), improving on the previous best bound of tildeO(mn1/3 poly(1/ε)). Applying the same framework in the multicommodity setting solves a maximum concurrent multicommodity flow problem with k commodities in O(m1+o(1)ε-2k2) time, improving on the existing bound of tildeO(m4/3 poly(k,ε-1). Our algorithms utilize several new technical tools that we believe may be of independent interest: - We give a non-Euclidean generalization of gradient descent and provide bounds on its performance. Using this, we show how to reduce approximate maximum flow and maximum concurrent flow to the efficient construction of oblivious routings with a low competitive ratio. - We define and provide an efficient construction of a new type of flow sparsifier. In addition to providing the standard properties of a cut sparsifier our construction allows for flows in the sparse graph to be routed (very efficiently) in the original graph with low congestion. - We give the first almost-linear-time construction of an O(mo(1))-competitive oblivious routing scheme. No previous such algorithm ran in time better than tildeOmega(mn). We also note that independently Jonah Sherman produced an almost linear time algorithm for maximum flow and we thank him for coordinating submissions.
Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2013.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 4; 2025: 8; 2026: 3.
18 papers.
Sort:
- 2025Training Deep Learning Models with Norm-Constrained LMOscited by 177
- 2025Generalized Gradient Norm Clipping & Non-Euclidean (L0,L1)-Smoothnesscited by 17
- 2022Private Convex Optimization in General Normscited by 13
- 2025A Tale of Two Geometries: Adaptive Optimizers and Non-Euclidean Descentcited by 5
- 2026Clarifying Shampoo: Adapting Spectral Descent to Stochasticity and the Parameter Trajectorycited by 5
- 2025Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimizationcited by 3
- 2025Provable Benefit of SignGD: A Minimal Model Under Heavy-Tailed Class Imbalancecited by 3
- 2024Top-K ranking with a monotone adversarycited by 2
- 2024Faster Acceleration for Steepest Descentcited by 1
- 2025Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learningcited by 1
- 2026Free Heavy-Tailed Lunch for Muon: A Theoretical Justification of Empirical Successcited by 1
- 2021ℓ2-norm Flow Diffusion in Near-Linear Timecited by 0
6 more
- 2021Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practicecited by 0
- 2024Expander Hierarchies for Normalized Cuts on Graphscited by 0
- 2024SHyPar: A Spectral Coarsening Approach to Hypergraph Partitioningcited by 0
- 2025Smooth Quadratic Prediction Marketscited by 0
- 2025Adaptive Conditional Gradient Descentcited by 0
- 2026Functional Stochastic Localizationcited by 0
- 2026Free Heavy-Tailed Lunch for Muon: A Theoretical Justification of Empirical Success12 Jun 2026
- 2026Clarifying Shampoo: Adapting Spectral Descent to Stochasticity and the Parameter Trajectory10 Feb 2026
- 2026Functional Stochastic Localization3 Feb 2026
- 2025Provable Benefit of SignGD: A Minimal Model Under Heavy-Tailed Class Imbalance30 Nov 2025
- 2025A Tale of Two Geometries: Adaptive Optimizers and Non-Euclidean Descent25 Nov 2025
- 2025Adaptive Conditional Gradient Descent13 Oct 2025
- 2025Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning6 Jul 2025
- 2025Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimization7 Jun 2025
- 2025Generalized Gradient Norm Clipping & Non-Euclidean (L0,L1)-Smoothness2 Jun 2025
- 2025Smooth Quadratic Prediction Markets5 May 2025
- 2025Training Deep Learning Models with Norm-Constrained LMOs11 Feb 2025
- 2024SHyPar: A Spectral Coarsening Approach to Hypergraph Partitioning9 Oct 2024
6 more
- 2024Faster Acceleration for Steepest Descent28 Sep 2024
- 2024Expander Hierarchies for Normalized Cuts on Graphs20 Jun 2024
- 2024Top-K ranking with a monotone adversary12 Feb 2024
- 2022Private Convex Optimization in General Norms18 Jul 2022
- 2021Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice5 Oct 2021
- 2021ℓ2-norm Flow Diffusion in Near-Linear Time30 May 2021
- 2025Training Deep Learning Models with Norm-Constrained LMOscited by 177
- 2025Generalized Gradient Norm Clipping & Non-Euclidean (L0,L1)-Smoothnesscited by 17
- 2022Private Convex Optimization in General Normscited by 13
- 2025A Tale of Two Geometries: Adaptive Optimizers and Non-Euclidean Descentcited by 5
- 2026Clarifying Shampoo: Adapting Spectral Descent to Stochasticity and the Parameter Trajectorycited by 5
- 2025Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimizationcited by 3
- 2025Provable Benefit of SignGD: A Minimal Model Under Heavy-Tailed Class Imbalancecited by 3
- 2024Top-K ranking with a monotone adversarycited by 2
- 2024Faster Acceleration for Steepest Descentcited by 1
- 2025Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learningcited by 1
- 2026Free Heavy-Tailed Lunch for Muon: A Theoretical Justification of Empirical Successcited by 1
- 2021ℓ2-norm Flow Diffusion in Near-Linear Timecited by 0
12 most cited of 18 All 18 (JSON)
- 2026Free Heavy-Tailed Lunch for Muon: A Theoretical Justification of Empirical Success12 Jun 2026
- 2026Clarifying Shampoo: Adapting Spectral Descent to Stochasticity and the Parameter Trajectory10 Feb 2026
- 2026Functional Stochastic Localization3 Feb 2026
- 2025Provable Benefit of SignGD: A Minimal Model Under Heavy-Tailed Class Imbalance30 Nov 2025
- 2025A Tale of Two Geometries: Adaptive Optimizers and Non-Euclidean Descent25 Nov 2025
- 2025Adaptive Conditional Gradient Descent13 Oct 2025
- 2025Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning6 Jul 2025
- 2025Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimization7 Jun 2025
- 2025Generalized Gradient Norm Clipping & Non-Euclidean (L0,L1)-Smoothness2 Jun 2025
- 2025Smooth Quadratic Prediction Markets5 May 2025
- 2025Training Deep Learning Models with Norm-Constrained LMOs11 Feb 2025
- 2024SHyPar: A Spectral Coarsening Approach to Hypergraph Partitioning9 Oct 2024
12 newest of 18 All 18 (JSON)